While most of what he's spotted recently have been known crafts deployed by the National Reconnaissance Office (NRO), including the Boeing-built Future Image Architecture (FIA)-Radar satellites and some Keyhole (KH)-11 orbiters, one stood out to Schöfbänker as nothing he's seen before.

Man Spots Secret US Military Spacecraft With Amateur Telescope

It was a calm early morning in Eilat, Israel&#x27;s southernmost city, where the dry Negev desert meets the warm waters of the Red Sea. Far to the north, the &quot;Iron Sword&quot; war raged, yet here, peace remained undisturbed. However, an unexpected drama unfolded on 31 October 2023, ...

Green Pine radar: the key to Israel’s ballistic missile defense | Paid content | Flight Global

HOUSTON – Thunderstorms, and light showers in parts of NW Houston toward Jersey Village. Our radars are also showing more activity on the east side and we’re expecting for these showers to strengthen tonight.

LIVE RADAR: Thunderstorms developing in Houston, expected to strengthen tonight

A pro-Moscow military blogger said the attack involved 12 ATACMS missiles launched in two waves.

Crimea strike smashed S-400 systems, radar stations and control center—Kyiv

The governor of Russia&#x27;s Mordovia region, where Moscow bases its Container over-the-horizon radar, said drones had &quot;again attacked&quot; a facility in the region.

Russia loses pivotal nuclear deterrent radar after drone attack: Report

Ukraine destroyed several units of military equipment in an attack on a Russian military airfield in Dzhankoi in occupied Crimea on April 17, Ukraine&#x27;s military intelligence (HUR) reported.

Military intelligence: Ukraine destroys missile launchers, radar stations at Russian military airfield in Crimea

Treks new CarBack radar boasts a slew of features, some best in class, including spotting rear-approaching vehicles up to 240km away

Trek Enters the Radar Game With CarBack Bike Radar - Bikerumor

Impact of study on US military’s F-22 fighter jet could be great given range of its air-to-air missiles and required radius for ground bomb attacks, says team.

Chinese scientists increase F-22 fighter jet’s radar signature 60,000 times with new detection method: study | South China Morning Post

The second target for Ukraine’s explosives-laden robotic sports plane is a big one: a 1,900-mile-range over-the-horizon radar in the Russian town of Kovylkino.

Ukraine Aims Its Explosive Sport Plane Drones At Russia’s Only 1,900-Mile Strategic Radar

Ukrainian drones attacked a military unit in the city of Kovylkino in Russia&#x27;s Mordovia Republic, hitting a Container radar station on April 17, a source in Ukraine&#x27;s military intelligence agency (HUR) told the Kyiv Independent.

Source: Ukraine strikes radar station in Russia's Mordovia Republic

Radar

radar

Sensor fusion is the process of combining data from multiple physical sensors in real time, while also adding information from mathematical models, to create an accurate picture of the local environment. A system can then use this data to plan and act toward an objective or destination. Sensor fusion is an important part of the design of autonomous systems. The cost of sensors has been declining for decades, while the quality of information collected by sensors has been increasing. Still, each type of sensor has its shortcomings. Even if a sensor provides a significant volume of high-quality data, the sensor could be thrown off under some conditions, leading to inaccurate readings. For example, a sensor on an autonomous vehicle could be thrown off by unusual weather, smog and pollution, speed, altitude, visibility, angle and positioning. By adding more sensors, engineers can often improve the accuracy of collected data. The Process: Sense, Perceive, Plan, Act, Repeat Sensors “sense” by collecting data from the physical world. Then, systems “perceive”, or interpret this data based on algorithms, use cases and requirements. Next, they “plan”, or find a path to move forward toward the desired outcome or destination. Lastly, based on their plans, they “act”, or follow a path toward the intended destination or outcome. The system goes on repeating these steps until it accomplishes its task or reaches its destination. Direct and Indirect Sensor Fusion Direct fusion happens when data originates from identical sensors in a given environment, while indirect fusion occurs when data originates from non-identical sensors in a given environment. For improved environmental awareness, it is important to boost both the quality and the quantity of data collected. So, ideally, a system should use both direct and indirect sensor fusion. Sensor Types Sensor fusion can be achieved using a wide variety of sensors. These include: accelerometers, GPS, magnetic sensors, phased arrays, electronic support measures, seismic sensors, sonobuoys, radio telescopes, cameras, radar, LIDAR and sonar systems. Benefits of Sensor Fusion Sensor fusion has several benefits: It usually increases the quality of data a system receives, as discussed It increases system reliability It can help estimate unmeasured states It can be used to increase the coverage area It reduces latency in the system It increases the ability to share data Use Cases of Sensor Fusion Today, sensor fusion is primarily used in autonomous vehicles like self-driving cars. However, research and testing is underway to use sensor fusion in other use cases in fields like space exploration, remote search and rescue, industrial internet, defense, and environmental monitoring. The field of sensor fusion is also seeing increased interest among academics and roboticists. Conclusion Sensor fusion increases the number of identical sensors, diversifies the types of sensors, and uses mathematical models to synthesize and refine information collected by sensors. Since sensor fusion enables more accurate localization, positioning, detecting and tracking, it improves autonomous systems’ situational awareness and makes the systems more consistent, accurate and dependable. The use of sensor fusion in autonomous vehicles will lead to significant innovation and know-how, which will find application in a wide variety of industries. Our mission at is to help companies create new value and discover new business models. We work with our clients to identify emerging opportunities and develop viable new offerings that drive market success. Learn more and follow me on Twitter @ Lansaar Research here Shaan Ray Previously published at https://www.lansaar.com/blog/what-is-sensor-fusion

What Is Sensor Fusion?

BUIDLing Your Next Great AI dApp? These 9 'Ethereum Alternatives' Should Be on Your Radar.

আপনার পরবর্তী দুর্দান্ত AI dApp তৈরি করছেন? এই 9টি 'ইথেরিয়াম বিকল্প' আপনার রাডারে থাকা উচিত।

Sie bauen Ihre nächste großartige KI-dApp? Diese 9 „Ethereum-Alternativen“ sollten Sie auf dem Schirm haben.

¿Está desarrollando su próxima gran aplicación de inteligencia artificial descentralizada? Estas 9 "alternativas a Ethereum" deberían estar en su radar.

Vous êtes en train de créer votre prochaine grande dApp IA ? Ces 9 « alternatives à Ethereum » devraient figurer sur votre radar.

अपना अगला बढ़िया AI dApp बनाना चाहते हैं? ये 9 'एथेरियम विकल्प' आपके रडार पर होने चाहिए।

次の素晴らしい AI dApp を構築していますか? 注目すべき 9 つの「Ethereum の代替」。

다음의 훌륭한 AI dApp을 구축하고 계신가요? 이 9가지 '이더리움 대안'이 여러분의 레이더에 있어야 합니다.

CONSTRUINDO seu próximo grande dApp de IA? Essas 9 'Alternativas ao Ethereum' devem estar no seu radar.

СОЗДАЕТЕ свое следующее великолепное dApp на базе искусственного интеллекта? Эти 9 «альтернатив Ethereum» должны быть на вашем радаре.

Bir Sonraki Harika Yapay Zeka dApp'inizi mi İnşa Ediyorsunuz? Radarınızda Olması Gereken Bu 9 'Ethereum Alternatifi'

XÂY DỰNG DApp AI tuyệt vời tiếp theo của bạn? 9 'Giải pháp thay thế Ethereum' này nên nằm trong tầm ngắm của bạn.

正在构建您的下一个伟大的 AI dApp？这 9 个“以太坊替代品”应该引起您的关注。

Deep Learning: Edge Devices With mmWave FMCW Radars Part 1 - Signal Processing

ডিপ লার্নিং: mmWave FMCW রাডার সহ এজ ডিভাইস পার্ট 1 - সিগন্যাল প্রসেসিং

Deep Learning: Edge-Geräte mit mmWave-FMCW-Radaren, Teil 1 – Signalverarbeitung

Aprendizaje profundo: dispositivos perimetrales con radares mmWave FMCW Parte 1: Procesamiento de señales

Apprentissage profond : appareils Edge avec radars mmWave FMCW, partie 1 - Traitement du signal

डीप लर्निंग: एमएमवेव एफएमसीडब्लू रडार के साथ एज डिवाइस भाग 1 - सिग्नल प्रोसेसिंग

ディープラーニング: mmWave FMCW レーダーを搭載したエッジデバイス パート 1 - 信号処理

딥 러닝: mmWave FMCW 레이더를 사용한 엣지 장치 1부 - 신호 처리

Aprendizado profundo: dispositivos de borda com radares FMCW mmWave Parte 1 - Processamento de sinal

Глубокое обучение: периферийные устройства с радарами mmWave FMCW. Часть 1. Обработка сигналов

Derin Öğrenme: mmWave FMCW Radarlarına Sahip Uç Cihazlar Bölüm 1 - Sinyal İşleme

Học sâu: Thiết bị biên có radar mmWave FMCW Phần 1 - Xử lý tín hiệu

深度学习：配备毫米波 FMCW 雷达的边缘设备第 1 部分 - 信号处理

AI is totally changing the game for software developers these days. There are some crazy cool AI tools out there that can seriously level up any programmer's workflow. These aren't just nice extras. They can be total game-changers for modern development work. Any developer looking to step up their game should seriously consider integrating these AI capabilities into their process. AI tools Of all the useful AI tools that exist today, here are six that stand out among the crowd that every developer should absolutely have on their radar. 1. ai-shell 💬 ai-shell is an open-source CLI tool that translates natural language into shell commands. Think of it as having a conversation with your terminal, where you describe what you want, and it provides the relevant command. What it does: Developers unfamiliar with specific shell commands or those who prefer a more intuitive interaction with the terminal. Who is it for: Star ai-shell on Github 🌟 https://github.com/BuilderIO/ai-shell 2. Cursor 💻 Cursor integrates AI directly into the coding process, helping in various tasks. It assists in code changes, bug spotting, debugging, and even offers a chat feature to ask questions about your codebase. What it does: Developers seeking an enhanced coding experience with the power of AI. Who is it for: Or if you want to experience some magic in your IDE, honestly- I just love watching it go. Website: https://cursor.sh 3. v0 🎨 Imagine being able to craft UI components with just a simple text prompt. v0 makes this a reality, allowing developers to swiftly generate a range of UI elements, from chat interfaces to dashboards. What it does: Front-end developers and UI/UX designers looking for a quick design solution. Who is it for: Website: https://v0.dev 4. Pezzo ⚙️ Pezzo is a cloud-native LLMOps platform for building, monitoring, optimizing, and deploying AI features. It offers prompt management, troubleshooting capabilities, and instant prompt change delivery. What it does: Developers who work with language models and seek a unified platform for AI operations with first-class TypeScript Support. Who is it for: Star Pezzo on Github 🌟 https://github.com/pezzolabs/pezzo 5. Tolgee 🌐 Tolgee streamlines the software localization process, making it easier for developers to offer their applications in multiple languages. It offers in-context translating, one-click screenshots, and integrates with various other tools. What it does: Developers working on a product with a global audience and wanting an efficient tool for software translation and localization. Who is it for: Star Tolgee on Github 🌟 https://github.com/tolgee/tolgee-platform 6. Formbricks 📝 Formbricks is an open-source survey toolbox designed for creating in-product micro-surveys. It's a powerful tool to gather insights from users, enhancing the product's user experience. What it does: Product managers and developers keen on understanding their users' needs and enhancing product experience. Who is it for: Star Formbricks on Github 🌟 https://github.com/formbricks/formbricks These tools are just the tip of the iceberg, and as AI continues to advance, I'm sure even more groundbreaking tools will emerge in the near future. So, stay curious and keep exploring! 🚀 Also published . here

6 AI Tools Software Devs Should Have On Their Radar

6টি AI টুল সফ্টওয়্যার দেব তাদের রাডারে থাকা উচিত

6 KI-Tools, die Softwareentwickler auf ihrem Radar haben sollten

6 herramientas de inteligencia artificial que los desarrolladores de software deberían tener en su radar

6 développeurs de logiciels d’outils d’IA devraient avoir sur leur radar

6 एआई उपकरण सॉफ्टवेयर डेवलपर्स को अपने रडार पर रखने चाहिए

ソフトウェア開発者が注目すべき 6 つの AI ツール

소프트웨어 개발자가 염두에 두어야 할 6가지 AI 도구

6 desenvolvedores de software de ferramentas de IA devem ter em seu radar

6 инструментов искусственного интеллекта, которые должны быть в центре внимания разработчиков программного обеспечения

Yazılım Geliştiricilerin Radarında Bulunması Gereken 6 Yapay Zeka Aracı

6 công cụ AI mà nhà phát triển phần mềm nên có trên radar của họ

软件开发人员应该关注的 6 个人工智能工具

is a regular time to take a look at what experienced industry experts think might be the next wave of tools, practices, and technologies to consider or stop using. Thoughtworks’ Technology Radar I always look forward to adding many new “blips” to my list, or seeing which are growing in adoption since you last took a look. Version 27 is out now, and it continues some trends that the radar has been following for a while, such as “platform as a service” or forms of group programming. This round-up is far from comprehensive, but just the blips that appealed to me or that, I thought, might appeal to you. If any of them take your interest, or you have an opinion on anything I’ve featured, ! head over to your profile and let our readers know Mainstreaming of Machine Learning It really does feel like that in the past 6 months or so, machine learning went from something that mostly only developers and data scientists interacted with to something that the public had awareness of. There was announcement after announcement of another ML or AI-based tool that spat out some sort of media after you fed something into it, and we were swamped with thousands of strange-looking images and videos of celebrities. This also means that developers interested in experimenting with ML also have a lot more resources available to them in the shape of data sets and frameworks to use those data sets. Of relevance to experimenting developers is , a field of machine learning enabling a variety of always-on use-cases and targeting battery-operated devices. TinyML Another is , a method for creating ML pipelines that might be more familiar to developers from other backgrounds. feature stores This radar also covered ML-related tools for improving the developer experience of building and training models, for example, the practice of “federated machine learning” for aggregating model data across machines for processing or synthetic data for initial model testing. Continuous Observability Despite being a relatively new term, “observability” has rapidly grown from a loose collection of practices and standards for building a better picture of a running system to become a broader term to mean, well, to mean all sorts of things. The latest area of the developer workflow to receive insights is one that I am surprised took so long, observability for continuous integration and delivery pipelines. While CI and CD certainly bring benefits, processes frequently go wrong with very little insight into what until you try, try again. Now, a handful of existing observability vendors and open-source projects are bringing tracing-style visualizations of continuous processes to hopefully help you debug quite what was going on. Software Bill of Materials As developers lean increasingly on semi-anonymous containers and packages for their work, those focused on security and legalities looked on and wondered, “do we trust this?”. This concern has led to a rush to standardize and normalize (in enterprise, especially) a “software bill of materials”, often referred to by the comical acronym, SBOMs. While, in theory, “anyone” can dig into an open-source project and see what its components are, this is generally easier said than done and isn’t possible with closed-source projects at all. Recent high-profile vulnerabilities in software revealed how small dependencies can bring such large problems. While SBOMs could go a long way to help, I wonder how many companies will want to reveal their inner workings and be honest about them. Sustainable Coding This is a topic that I was inspired to cover myself after speaking with someone from Thoughtworks about their Carbon cloud footprint tool. The tendency to endlessly throw new frameworks, tools, and cloud frameworks at a problem has led to many of us forgetting that all of these lines of code actually run somewhere on something. And that leads to carbon emissions. Water motivated by altruistic reasons or financial, and an ecumenic downturn leads to cost savings, this is a topic more are talking and thinking about. Closely related to the environmental cost of projects and their infrastructure, and an option I’ve seen used by production teams is which estimates the cost impact of changes you make to Terraform definitions. infracost Responsibilities as Code Any SaaS company defines service-level indicators (SLIs) and service-level objectives (SLOs) that their clients can expect. However, how companies defined these have often been a bit haphazard and non-standard, and with so much else defined in codified pipelines, why not this fundamental component too? Several Observability providers now offer this option and of course, a group of people started a standard, . OpenSLO A No to Node I dabble in JavaScript and TypeScript and have noticed a handful of emerging runtime alternatives to Node recently. I am not sure what is wrong with Node, aside from being fairly old now, to warrant alternatives, but I guess competition is good 🤷‍♂️? is one newer alternative that I tried a few months ago, and I’ll admit I wasn’t really sure what benefits it brought me, but it uses WebKit’s JavaScriptCore instead of Chrome’s V8 engine. Bun Again, variety isn’t a bad thing. Bun is written in , which I had never heard of before, and it claims to be a “drop-in” C/C++ replacement. Zig Which leads nicely to… C+++ If Zig, Rust, and other languages are anything to go by, numerous people feel that C++ needs a more modern replacement. Google has thrown another option into the list of contenders with . Carbon The language is still in experimental stages but claims to be easier to understand and onboard with. Google has a tendency to spin up many experimental projects and then dump them again, so Thoughtworks’ suggestion is currently “wait and see”.

Developer Trends from Thoughtworks’ Technology Radar 27

Tendencias de desarrolladores del Radar de tecnología de Thoughtworks 27

Tendances des développeurs du radar technologique de Thoughtworks 27

थॉटवर्क्स के टेक्नोलॉजी रडार 27 से डेवलपर रुझान

ソートワークスのテクノロジー レーダー 27 からの開発者トレンド

Tendências de desenvolvedores do Technology Radar 27 da Thoughtworks

Xu hướng dành cho nhà phát triển từ Radar công nghệ của Thoughtworks 27

Thoughtworks 技术雷达的开发者趋势 27

Radar detectors matter more than ever, but you wouldn’t know it from the poorly researched clickbait that was Doug Demuro’s recent article, “Radar Detectors Are Useless Now.” I like DeMuro. We’ve met. I’ve been reading him since he started at Jalopnik. At his best, Demuro is the Dave Barry of automotive. I laughed out loud at DeMuro’s Plays With Cars. He’s a great entertainer. I also know he’s not an idiot, so I can only assume he was too lazy to Google some basic facts, and willing to sacrifice his readers’ wallets on the altar of alternative facts. Sad! Why should you trust my opinion? Because I led the team that broke the Cannonball Run record in 2007, driving from New York to Los Angeles in 31 hours and 4 minutes. I also led the teams that set the electric and semi-autonomous records in 2016 (55 hours), the 3-wheeled record (41 hours), the Key West to Seattle record (45 hours, 24 minutes), and similar records across Sweden, Spain and Portugal. How many tickets did I get? Zero. If you like speeding tickets, higher insurance premiums, giving your money to the government and trusting people you’ve never met, DeMuro’s your guy. If you don’t, let’s learn why detectors still matter by deconstructing DeMuro’s article line-by-line, starting with the headline: "Radar Detectors Are Useless Now" Did DeMuro consult with anyone before his making this sweeping generalization? and have tens of thousands of posts from avid detector users. If he needed a second or third opinion on the efficacy of detectors, not only does he have my phone number, he’s friends with Ed Bolian, who broke our Cannonball Run record in 2013 using the same detector I use. RDforum.com RadarDetector.net Check this out for the reviews on radar detectors 2020 I've recently come to a conclusion about radar detectors—an item that many car enthusiasts have considered crucial to avoiding speeding tickets for the last few decades. And my conclusion is: these days, in these modern times, they're useless. It's over. There's simply no point in having a radar detector anymore. I get emails on a daily basis from car enthusiasts asking what detector to buy. Does DeMuro? Maybe this was his way on cutting down on e-mails and comments he didn’t want to (and couldn’t) answer. That opening paragraph is perfectly constructed to maximize SEO. That last sentence? A beautiful slug complementing the title in Google search results. I originally started reaching this conclusion a few months ago, when I drove across the country and back in my radar detector-equipped Aston Martin V8 Vantage. On this 6,000-plus mile road trip, I discovered two very important things: One, radar detectors are being beaten at their own game. And two, radar detectors don't really work on modern roads. What kind of radar detector did DeMuro use? He doesn’t say. Not all radar detectors are the same. Many are expensive bricks. If it was anything other than a Valentine 1 or top-of-the-line Escort, he’s a fool. Speeding with a lesser detector is like smoking on the way to chemo, or wearing a helmet without a flak jacket, or buying used condoms in Vegas. Even if his detector was decent, everything that follows proves he didn’t read the manual, or learn how they work, or both. Technology is only as good as our understanding of it. Radar detectors are tools with limitations, like any tool. The invention of the power drill didn’t make screwdrivers obsolete, and no toolkit is complete without a hammer. Any project requires the very best of every tool available, and the basic intelligence not to use a hammer on a screw. Driving a car even one mile per hour over the speed limit requires a bit of preparation. An unlimited budget doesn’t guarantee victory at the track. Skill does. I'll start with number two: Radar detectors and modern roads don't really mesh all that well. Now, in the past when you got a radar detector, you could always count on it making its little chirping sounds and flashing its lights whenever you passed an automatic door (outside a shopping mall or a grocery store, for instance) because the technology they used was relatively similar to the technology radar detectors search for. And that makes sense. All those little chirping sounds and flashing lights that stymied DeMuro? They mean different things. You have to know what to listen for. Police radar operates in bands defined as X, K and Ka, and modern detectors can differentiate between them. Stationary radars used by door openers and burglar alarms generally operate in the X band. Older detectors — and cheaper detectors still sold today — are easily set off by X-band. That’s why detector manufacturers began offering “City” mode, which (hopefully) filters out most X-band signals. Modern detectors use Digital Signal Processing (DSP) to warn of X-band signals which may still pose a threat, but since most police have migrated to K and Ka, X-band is less of a concern than it used to be. From the early 90’s until recently, the state-of-the-art radar detector was the Valentine One. It was the only radar detector with a directional display with arrows. In its heyday, it made every other detector look like junk. A user with a bit of intelligence and experience could easily discern from the arrows whether an alert was an actual threat. The ability to interpret feedback from tools is called judgment, much like how serious drivers interpret steering feel and tire noise to determine grip. Front or rear alert? More likely to be a cop. Side alerts? Less likely. More importantly, the V1 was also the first detector that was hardware upgradeable. As police radar (and sources of false alarms) evolved, so did the V1. I’ve used a V1 on every single cross-country record to date. The results speak for themselves. The very best detectors may have GPS and a lockout feature, allowing users to tag false alarms by location. This works best for those who use detectors on commutes, but does nothing for those on a road they’ve never driven unless the device is networked. The latest solution? Connected detectors like Escort’s Max 360, which, when used in conjunction with the Escort Live subscription service, crowdsource data — whether police or false alarms — across the network. But these days, it isn't just shopping malls. Here in 2017, virtually every modern vehicle on the road is equipped with blind spot monitoring, parking sensors, adaptive cruise control and lane keep assist, and all of those systems seem to use some form of technology that makes a radar detector go crazy with flashing and chirping. Seriously: Take a radar detector down a road in a nice area, where people tend to have new cars. Every time you see a Mercedes, or a BMW -- in fact, most Fords and Chevys, these days -- your radar detector will freak out. DeMuro is partially correct, but appears ignorant of progress on the detector side. Safety features on new cars can emit signals that set off detectors, and fixed GPS lockouts can’t address them. Today’s lesser detectors are often set off by cars whose radar-based safety systems overlap with K and Ka-band police radar, but a Google search (that took six seconds) unearthed this 2015 article explaining what DeMuro failed to mention: buy a V1 or halo Escort product, and the problem is at least partially solved. Valentine One offers a “Junk K-Fighter” hardware upgrade, and Escort’s Max 360 — which I’ve been evaluating side-by-side with the V1 for the last year — is firmware updatable. Download and install their latest database incorporating the digital fingerprints of offending mobile radar sources, and the Max 360 will filter them out from police radar threats. Failing to update your detector is like running Windows 95 in 2017, or using an old anti-virus database. Amateur. The number of false positives has gone from "annoying but acceptable" to "please shut up so I can listen to my music." If eight out of every nine radar detector chirps and flashes are fake, you start to simply not trust it. And then, what's the point? If you’re driving fast enough to need a detector, you shouldn’t be listening to music. Visual warnings are seldom sufficiently clear to identify and interpret the type of incoming signal, especially if you’ve wisely mounted your detector slightly off-axis. You don’t want it in front of your head, in case you have an accident, which means you need the loudest possible warnings. And it's not just the false positives. Modern radar detectors are expensive… Wrong. Modern detectors are cheap compared to the cost of potential tickets, court fees and increased insurance premiums over the lifetime of the very best units. They’re a lot cheaper than using Uber or Lyft for the duration of a license suspension. A V1 with a smartphone interface and concealed display kit — both mandatory items for the serious user — is approximately $500. An Escort Max 360 — which includes the utterly essential directional display pioneered by the V1 — is about $700 with a phone interface. Add $50/year for the mandatory Premium Escort Live crowdsourcing subscription, and you possess a start-of-the-art tool. Are they perfect? No tool is, but they’re the best ever made, and totally up to the task when used correctly.  But there’s more to that story. ...and they're being beaten at their own game by much cheaper solutions. The best example of this, of course, is Waze -- a mobile app that lets users report police presence (and other potential road dangers) so you know when to slow down in order to avoid a ticket. I've noticed two things about Waze: One, that it's tremendously accurate. And two, that it's tremendously quick. Almost as quick as a radar detector, and certainly with fewer false positives. DeMuro thinks that because detectors aren’t infallible, a free application like Waze is an appropriate substitute. That’s like saying that if snow tires don’t give you enough grip, you should put chains on summer tires. Waze isn’t — and never will be — a substitute for a radar detector. It is, however, the world’s greatest complementary tool for anyone who knows how to use a good detector, if you understand how Waze works. The crowdsourced data in Waze is only as accurate as the minority of users who bother to enter police locations. Waze data is totally porous. If no users are in the vicinity, no data. Even in a large group of users, one is dependent on multiple users accurately corroborating data over a short span of time in your vicinity. Waze can’t solve problems a good detector will. No amount of crowdsourcing can solve for a police car hidden behind a bridge. What if users don’t recognize an undercover car in traffic? And what if they do? Waze offers no method of tracking police cars in motion. If multiple users enter a moving police car, it appears as multiple stationary traps on the map. Accurate? Not even close. Here’s another Waze problem. Data entry makes driving less safe. Even if you have a professional phone mount — and most don’t — entering data accurately requires navigating a less than stellar UI. Serious drivers — as in the ones most aware of the dangers of handheld phone use — are also those most likely to enter accurate data, yet the least likely to take their eyes off the road to do so. Even I — who want to help fellow Wazers as much as possible — am not always able to safely. The good news? if you use Waze AND a state-of-the-art detector, you command a set of tools vastly more powerful than what law enforcement can deploy on the open road today. While [Waze] suffers from some false positives as a result of taking a while to remove reports of police vehicles that may be looking for speeding drivers, Waze is generally pretty accurate -- and pretty quick -- at recognizing speed traps. If you're cruising down the highway and Waze tells you there's a police car ahead, you're likely to slow down -- a lot more so than if you're cruising down the highway and your radar detector goes off. When your radar detector goes off, the first thing you do is you start looking around for a car with adaptive cruise control. No, Doug. Those are things you do, which are totally amateur. As the Albanian said to Liam Neeson in Taken: GOOD LUCK. DeMuro is wrong on every point. That second sentence makes no sense. Waze places and retains police reports over time based user corroboration. If you’re the first speeder to happen upon a cop moving from the location where they were tagged, you’re out of luck. Also, nothing is to prevent a false positive created by a Waze troll, like the cops who tagged empty locations to slow down traffic. What about someone who makes a mistake tagging a cop at speed and enters “other side” instead of “my side” in the silly UI? As for slowing down every time Waze goes off, I do so only if corroborated by other factors like traffic, terrain and the recency of my detector alerts. That’s called judgment. All of this brings me back to my cross-country road trip. Early in the trip, I had the radar detector switched on, convinced I'd be able to thwart any potential speeding tickets I might receive. As the trip went on, I gradually switched -- ditching the constant chirping and flashing of the radar detector for the soothing sounds of the person in the Waze app. By the end of the trip, I was all Waze -- and I've never looked back. And, frankly, I don't think I ever will. Goodbye, chirping radar detectors. The modern world doesn't need you anymore. DeMuro thought his detector would thwart any potential speeding tickets? Condoms break sometimes. Does that mean no one should use them? Hey Doug, what do you think will happen if no one uses detectors anymore? I’ll tell you. A large minority of Waze-tagged speed traps will disappear. Why? Because the cop hiding behind a bridge can’t be tagged by Wazers who aren’t looking for him. A detector may be the only warning of their presence. Power detector users — the ones using Waze at the same time — are therefore Wazers’ best allies. If technology is only as good as our understanding of it, then driving is only as safe as the user’s skill. Driving at any speed can dangerous. Driving over the limit introduces risks that only a radar detector and crowd-sourced solution used simultaneously can mitigate. Using these tools correctly takes work; more work than a columnist too lazy to use Google seems capable of. It’s an investment in time and money not everyone is prepared to make. If you’re not ready, go ahead, skip the detector and use Waze, but don’t bother going over the speed limit. Waze alone is worse than using the best detector on its own. Placing faith in technology we don’t entirely understand can lead to a lot more than tickets. It’s what led to Josh Brown’s Tesla crash, and Air France 447. The art and science of integrating a detector and Waze into your driving regimen are too complex to fit here, but I suggest starting with The Rules of Professional Speeding.

Why We Need Radar Detectors

Radar detectors are like a blessing for the drivers as these are small devices which help you in the identification of the presence of radars around you, kind of what the cops use to identify the speeding drivers. There are a few types of radar detectors who help you to identify the presence of laser guns around you and save you from any kind of speed traps or whatsoever. For you to understand how to use the radar detectors and how radar detectors work, we have dedicated this blog post. Keep on reading. How do radars work? The use of radars is various. They are frequently used for the purpose of identifying the distance and speed like that of how far away an object is or how fast it is moving. A radar also emits the radio waves which moves at the speed of light and then bounces back to the device when it encounters an object in its path. A basic radar detector usually identifies the radar devices based on the radio waves which it emits. These detectors simply act as the radio receivers who pick up the specific frequencies which are used by the radar devices which specifically includes the radar guns that are used by cops for the sake of identifying and catching the over speeding cars. As the radar detectors used by the cops cast a wide net of radio waves, the detectors in moving cars often pick up the radio waves before the car comes within close range of the cop cars to be detected. Simple method but works all the time. Usually, more of the advanced detectors not only detect the police radar, but can also throw off the reading which a police detector usually receives. Such type of radar detectors has a radio transmitter which emits a scrambled signal that takes the original detected signal and also adds the additional radio signals. And when the scrambled signal reaches the radar, the cops have trouble getting a pretty accurate speed reading and a similar system usually exists for the laser speed guns where the laser detector emits a beam of light for detection. And at last, let us tell you what different signals mean in your radar detector. Let us tell you that you might not be able to get the most from your radar detector in case it isn’t installed properly. You also need to be aware of different signals which your radar shows. Here are some of the signals in your radar detectors - •	Ka Band – About 70% of police radar guns use this particular band and thus, you need to pay close attention to your radar detector when such signals pop up there. •	K Band – Let us tell you that about 30% of police radar guns use this very band, which means you need to be aware when it pops up. •	X Band – This is a bit outdated one but still continues to be in use for some of the countries. •	Laser – Used for regular highway patrols. Lastly, as soon as your radar detector has identified the signals, reach as fast as you can as safety is the number one priority. Apply brakes slowly and watch out for any of the cops around and try to be in the speed limit as well.

How to use Radar Detectors?

First movers or fast followers — which has the advantage? When bitcoin was first proposed in 2008 it was revolutionary. For the first time people could trust in a currency without a bank or government behind it. Fast forward to 2018 and bitcoin is well above 10,000 USD a coin. Over 87% of people have heard of bitcoin. We’ve crossed the chasm. The first iPhone was released shortly before bitcoin was proposed. When bitcoin was conceived the app store was only just getting off the ground and portable DVD players were still used on long car trips. The technology of 2018 is leaps and bounds ahead of where it was a decade ago. Crypto technology is no exception. But bitcoin is almost a decade old. There are now over 1500 cryptocurrencies newer than bitcoin. These coins are more efficient, faster, and offer features that bitcoin lacks. In the past month bitcoin’s percentage of the market has fallen from 56% to 33%. Chart from show bitcoin’s decline in dominance over the past month. https://coinmarketcap.com/charts/ This is not the first time first movers have been threatened by newer technologies. Altavista gave way to google. Myspace fell to Facebook. There is a history of first movers losing to innovative fast followers. . Research shows that the first mover in a space is 6 times more likely to fail then the ventures that follow it Bitcoin has done the difficult work of trail-blazing, and now the floodgates are open for a slew of modern competitors. So if bitcoin is just the start of cryptocurrencies, what comes next? To understand what technologies could surpass bitcoin we need to understand where it is currently falling short. Bitcoin and its blockchain have serious flaws Transaction speed The bitcoin network can only handle 7 transactions a section. That means only 7 people worldwide can buy or sell using bitcoin every second. For comparison visa handles 24,000 transactions a second. Low transaction speed limits bitcoin’s effectiveness as a currency. It also hurts those who see bitcoin as a store of value. Exchanges go down and funds are stuck waiting hours (or even days) when too many people buy or sell bitcoin. Fees at the end of December were $37 a transaction, . Network congestion leads to extraordinarily high fees. more than the cost of a wire transfer 2. Lack of privacy Are you comfortable with the whole world knowing the value of your bank account? What about the whole world knowing where you send funds and who you receive them from? Bitcoin wallets are tied to address hashes (random sequences of letters). Once the owner of a wallet is identified all of their previous transactions and all of their future transactions are visible to anyone who downloads bitcoin’s ledger. This history can never be deleted or changed. 3. Programmability Bitcoin was designed as a digital currency. The technology allows only basic programming. This prevents bitcoin from being used in more complicated situations like for smart automatically executing contracts. Think of it like a calculator compared to a computer. Programmable blockchains (Ethereum being the most widely known example) have the potential to massively disrupt a wide array of sectors. Newer coins are introducing amazing new technologies The following are some of the most impressive contenders: Many of these coins are leaps and bounds ahead of bitcoins current capabilities. Insanely scalable. Great partnerships. Clear use case. Stellar Lumens (XLM)Pros: You’ve probably heard about Ripple amazing rally at the beginning of January. Stellar Lumens is the newer, 100% decentralized version of Ripple. Ripple’s CEO founded Stellar Lumens to address the flaws he saw in Ripple. It can handle 1000 transactions per second without breaking a sweat, and transfer millions of dollars for a few pennies. Its investors are none other than Y-Combinator’s CEO Sam Altman, and the CEO of Stripe. Insanely scalable. Instant transactions. Zero Fees. RaiBlocks (XRB)Pros: Raiblocks is an even newer coin — emerging only in the last few months. It uses a new form of architecture based on directed acyclic graphs that only a few cryptocurrencies have adopted. This makes it infinitely more scalable than blockchain applications. Their approach to development is to do ‘one thing well’. In this case they aim to be what bitcoin was meant to be: an instant and zero fee way of sending currency around the globe. Scalable and programable. ‘The Ethereum Killer’ EOS Pros: EOS has been jokingly described as ‘Ethereum on Steroids’. The developers behind EOS claim that the EOS blockchain is fully programmable, will eliminate transaction fees and have the ability to process millions of transactions a second. Their block chain launches in April. Bitcoin will have to drastically speed up development or be overtaken. Bitcoin’s last major update, scheduled in November, was canceled due to lack of community support. The November update was meant to introduce Segwit2x, a means of increasing the number of transactions that could be recorded on each block. Without Segwit2x Bitcoin’s networks have grown increasingly slow. The Lightning Network — Bitcoin’s now mythical response to scaling faces many unknowns before it can be implemented. More testing is necessary before it can foreseeably address some of Bitcoin’s shortcomings. In the meantime, up and comers like EOS, XLM, and XRB are ready to outperform Bitcoin with their speed and functionality. We have yet to see if these second-movers will take the market. The only thing we can know for sure is that the entire cryptocurrency space is rapidly iterating. The cryptocurrency that ends up winning may yet to be created. Enjoy this article? We have a Telegram Signal group now! We post . So you can spend less time doing technical analysis and more time 👉 simple and effective trading signals getting results. https://t.me/coinandcrypto

Bitcoin is outdated tech. These 3 alternatives should be on your radar.

A while ago released an for anyone to host their own personal radar. ThoughtWorks open source repository technology I was curious to see how the reputable company implemented it and strangely I noticed a lot of concepts that reminded me of Java, such as and error handling with . stateful objects try/catch/exception All the application really does is take a single input and produce a tech radar. A Java-esque approach seemed inappropriate for that use case so I decided to rewrite some of it in ! Elm Step 1: Model the radar Let’s start things off by thinking about how to model the radar itself. If we model our problem carefully it can simplify the work ahead of us. First let’s consider what we know about the data. The radar is split into and . These quadrants and rings represent different categories that several can be placed into. four quadrants four rings blips Since we know there are only four quadrants and rings let’s represent those as . union types So far so good. Next up, the blips. Modeling the blips requires some extra thinking because there are two representations of a blip. The first representation is the blip data from the Google Sheet ( ). The information included is the , , , an flag, and a . example name ring quadrant isNew description The blips that are visualized on the radar need those same 5 properties but in order to be displayed on the radar they also need an . x-y position Several ways to model these blips include: This option uses fewer types but will inevitably lead to more . Throw all properties into a single record and make the type of the position field a . Maybe maybe handling This option removes the but at the cost of in two separate record fields. Treat the blips from the Google Sheet as a distinct type and record from the blips visualized on the radar. maybe verbosity duplicating data Since the properties of the radar blip are a superset of the blips from the Google Sheet we can . A blip positioned on the radar will have the Google Sheet blip data in addition to a defined position. represent the data with composition There are other options worth considering, especially options that leverage union types, but the third option will suffice for now and we can refactor later if needed. Next up is visualizing the radar. If you’re curious on how we’re doing in comparison to ThoughtWorks’ open source example then swing on over to their OO equivalent modeling . Step 2: Visualize the radar Now that we have the basic building blocks to construct a view let’s start building the radar visualization. The first thing I want to do is get the quadrants and rings visualized. We’ll heavily use SVG and borrow a lot of the styles from the ThoughtWorks open source project. I’m sure they won’t mind. Creating an arc The radar isn’t a circle. It’s technically four quarter-circles put together, a.k.a arcs. Circles in SVG are not that hard. Arcs, on the other hand, suck big time. Here’s a demonstration of what I mean. With the above code working it will be exiled to a utility module so that we don’t have to think about the implementation details for creating an arc in SVG. Visualizing quadrants Visualizing a quadrant only requires that we visualize 4 arcs (rings), each with a different radius. Not bad. If we do this three more times and provide different numbers we get the entire radar background. Visualizing blips There are two types of blips that can be visualized which is determined by the flag. One is a rounded triangle and the other resembles a smudged circle. isNew I’m going to simply transcribe ThoughtWorks’ for these blip visualizations to Elm. code ..more SVG implementation details that we’re not going to look at ever again. Next up, visualizing mocked blips on the radar. Step 3: Wire up the view with mock data Before we consume actual data from a Google Sheet we’re going to mock up some data to make sure our blips are correctly displayed on the radar. The above function is but it will produce 16 blips on the radar, one in each ring in each quadrant. However, this brings up the question of where exactly to position the blips. temporary I went ahead and peeked at how . In short, they produce a sinusoidal curve that a blip is to be positioned on and if it collides with another blip it will try another random position on this curve. ThoughtWorks did it We can do the same thing in Elm but it brings up an interesting topic.. how to do randomness in a pure functional language. <quick detour> Pure randomness with generators In Elm, every function is a pure function. In order to produce randomness we can ask Elm to generate us a random value for a given seed. A random value and another seed is returned so that we can use that seed to ask for yet more random values. However, if we’re producing many random values then managing these seeds becomes extremely tedious and prone to mistakes. Fortunately, Elm gives us the concept of a . According to its documentation, Generator a generator is like a recipe for generating certain random values. Here’s an example generator that can be used to produce random integers from 3 to 7. Random.int 3 7 Here’s another generator for producing a random list of even values under 100. Simply creating a generator doesn’t actually produce a random value. It still has to be passed to . Random.step (randomEvens, nextSeed) =Random.step (generateEvenValues 10) (Random.initialSeed 123) Given this knowledge we can compose a few generators to randomly position blips along a sinusoidal curve and keep producing values if there is a collision. This will behave identically to the ThoughtWorks algorithm and it also happens to be where we convert the to . List GoogleSheetBlip List PositionedBlip Randomly positioned blips with Generators I realize we went from 0 to 60 with generators in about 90 seconds. If the above code is not immediately understandable, don’t worry. Like much with Elm, it is initially unfamiliar but it very quickly becomes second nature to read that code. </quick-detour> Blips visualized with randomness When we run the mock data at the beginning of this section into our view function we get a radar with blips on it. At this point we can keep throwing data at the radar and verify that the algorithm for determining blip positions is working. The positioning here seems reasonable. Moving on. If you’re tracking progress here’s how ThoughtWorks is doing the same . It’s also worth noting that the radar is mirrored along the y-axis because we hard coded the random seed. That’s fine. Step 4: Model the app state Just as before, modeling the app state is incredibly important. When writing your own Elm applications it’s worth pausing, discussing, and considering the trade-offs for how to model your data. For this application we already have a radar but we’re missing a page to enter a Google Sheet url. I chose to model that page state like this: It will have a form with an initially empty url. The form might have an error and we’ll want to know if we’re actively retrieving the Google Sheet. There are to this approach, but we’ll stick with these three properties for now. alternatives That covers what I’m calling the . Lastly, there’s the that needs to be provided to The Elm Architecture. Landing Page Model This is loosely based on except with a few things cut out for the sake of simplicity, such as not dealing with routes for such a small application. Richard Feldman’s SPA example It’s important to point out that at a high level this application is in one of two states: We’re on the landing page and interacting with the url input form We’re on the radar page interacting with a tech radar Step 5: User input, regex, data retrieval, and error handling The landing page involves , , , , and in all of those cases. I’m going to skip the user input part because if you made it this far you know how to do that already. receiving input from the user parsing out a Google Sheet ID performing an HTTP request parsing the response handling errors If I’m wrong and handling user input in Elm is new to you I suggest pausing and checking out this example . Regular expressions in Elm We’re about to go 0 to 60 again. Hang on. The user will supply us with a link to their published Google Sheet, such as “ ”. https://docs.google.com/spreadsheets/d/11Fd0lwNIEUs2ymxNEiTfpM5CoQHQbMuOId8TjrQHDn0/edit?usp=sharing Hidden within this URL is an ID that we need to perform an HTTP request. It’s that unreadable string between “/d/” and “/edit?”. First we’ll declare a regular expression that will find that ID. It goes without saying that credit for this regex goes to ThoughtWorks again. Within that regex are groups and we care about the first group specifically. In order to apply a regex to a string we use the function. find matches =find All sheetIdRegex url That returns a list of matches that take the following shape. Our Google Sheet ID is hidden within the list. So what we want to do is get the first match in the list of matches returned from , and on that first match find the first item in its . submatches find submatches If you’ve worked with Elm lists and before you’re probably already aware that we’re about to go head first into . Thanks to the code to pluck off that ID and produce a Result isn’t too bad. List.head MaybeLand elm-community/maybe-extra The first collapses the double Maybe that occurs when you do a on a List of Maybes. However, it’s still a Maybe returned from so the second collapses it one more time before converting it to a Result. MaybeExtra.join List.head Maybe.map MaybeExtra.join Retrieving the Google Sheet data Once we have the ID we can make a request to retrieve the Google Sheet data in CSV format. When this Cmd is performed it’ll call with the Result. httpResultToMsg The above function does the following On success, split the string by new lines Iterate over each row and attempt to parse the CSV as a blip Successfully parsing data puts it in the list while parse errors are added to the list. blips errors Send off a Msg with the blips and any errors Parsing each CSV row is as simple as attempting to map the data onto our data model defined earlier. Conclusion This post is getting long so I’ll wrap things up by making a few observations in rewriting some of the ThoughtWorks Tech Radar. The BYO radar project used for , for graphics and HTML templating, and for Google Sheets. It uses to model its data and incorporated rather than gracefully handling errors. chance randomness d3 tabletop parsing stateful object instances exception handling The code is fairly complicated and even with understanding the desired behavior it’s difficult to track the implementation details. Most of all, after spending so much time in that code I still don’t understand what much of the code does. On the other hand, a functional approach with Elm yielded more comprehensible code with similar functionality. In fact, once you grasp the way Elm does randomness and regular expressions you’ll realize the code is much more simple too. To see the partial rewrite in action check out these two links: http://ckoster22.github.io/elmradar/ https://github.com/ckoster22/techRadar/tree/blogpost

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